[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117355-en":3,"doc-seo-117355-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117355,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Effective machine learning in linguistics - read conference proceedings","Machine learning in modern linguistics is highly relevant for automating complex natural language processing, text analysis, and linguistic data processing tasks. Language models and ML algorithms enable systems to perform work that previously required substantial human resources and were difficult to accomplish. The approach accelerates processing of large textual volumes while improving efficiency and accuracy, supported by algorithmic adaptability. The paper connects future development to computational methods, access to qualitative data, and improved models for understanding language.","Proceedings of the 4th International Scientific and Practical Conference «Scientific Progressive Methods and Tools»  \n(October 6-8 , 2024) . Riga, Latvia  \nNo  \n219  \nPHILOLOGY AND LINGUISTICS  \nEffective machine learning in linguistics  \nKrasniuk Svitlana1  \n1 Senior Lecturer ;  \nDepartment of philology and translation,  \nKyiv National University of Technologies and Design ; Ukraine  \nAbstract. Machine learning (ML) in modern linguistics is extremely relevant and effective due to its ability to automate complex processes of natural language processing, text analysis, and linguistic data processing. Modern language models and machine learning algorithms are able to perform tasks that previously required significant human resources or were difficult to achieve. Machine learning is critical to modern linguistics, as it allows automating the processing of linguistic data, greatly increasing the efficiency and accuracy of linguistic research and practical applications. Its relevance lies in the need to process large volumes of textual information, and efficiency is ensured by the speed and adaptability of algorithms. The future of machine learning in linguistics is closely related to the development of computational methods, access to qualitative data, and the development of new models to better understand language.  \nKeywords: linguistics, machine learning, deep machine learning.  \nIntroduction.  \nMachine learning is a key technology in the framework of data mining, which allows automating the processes of analyzing large volumes of data, making accurate predictions, classifying and finding hidden structures in data [1, 2, 3] . Together, they create a powerful tool for working with information in various fields — from linguistics to business analytics, medicine, and social sciences [4, 5] .  \nMachine Learning is a subfield of artificial intelligence that aims to develop algorithms and models that allow computer systems to learn and improve their results on the basis of experience, that is, on the basis of data, without explicit programming for each specific task [6, 7, 8 , 9] .  \nThe relevance and importance of machine learning in linguistics is extremely high, since this approach opens up new opportunities for automating the analysis and processing of natural language, and also allows to significantly increase the efficiency of working with large arrays of text data.  \nMachine learning in linguistics is the application of artificial intelligence methods and algorithms for the  \nProceedings of the 4th International Scientific and Practical Conference «Scientific Progressive Methods and Tools»  \n(October 6-8 , 2024) . Riga, Latvia  \nNo  \n219  \nPHILOLOGY AND LINGUISTICS  \nanalysis of language data and the automation of natural language processing processes. This direction of research is a component of computational linguistics and includes the use of mathematical models to solve problems of a linguistic nature. Machine learning allows computer systems to learn on their own from existing data, doing so by identifying patterns and regularities in language structures.  \nThe main part.  \nThe main areas of application of machine learning in linguistics.  \n1. Natural Language Processing (NLP)  \nMachine learning is the basis for NLP, a field that aims to create systems that can understand, analyze and generate natural language. Examples of problems:  \n-Morphological analysis (detection of parts of speech) .  \n- Syntactic analysis (determining the grammatical structure of sentences) .  \n- Semantic analysis (recognizing the meaning of words and phrases in context) .  \n-Automatic summarization of texts.  \n2. Machine translation.  \nModern machine translation systems, such as Google Translate or DeepL, use neural networks to translate texts between different languages. Neural models are trained on huge text corpora of bilingual data, allowing them to translate texts more accurately and efficiently compared to previous methods.  \n3. Sentiment A","cbCailVzqkYJDjVW","https://ap.wps.com/l/cbCailVzqkYJDjVW","pdf",302943,1,6,"English","en",105,"# Introduction\n## Machine learning in linguistics\n# The main part\n## Main areas of application in linguistics\n## Types of machine learning algorithms in linguistics","[{\"question\":\"Why is machine learning considered important for modern linguistics?\",\"answer\":\"It automates natural language processing, text analysis, and linguistic data handling, improving efficiency and accuracy while enabling work that would otherwise require significant human effort.\"},{\"question\":\"Which application areas does the document highlight for machine learning in linguistics?\",\"answer\":\"It highlights natural language processing (morphology, syntax, semantics, summarization), machine translation, sentiment analysis, speech recognition, and text category identification.\"},{\"question\":\"What types of machine learning algorithms are discussed?\",\"answer\":\"The document describes frequency-feature based classification (e.g., bag-of-words, TF-IDF with Naive Bayes/SVM/logistic regression), neural networks (RNN/LSTM/GRU and Transformers like BERT/GPT), and word embeddings (Word2Vec, GloVe, FastText).\"}]","Effective machine learning in linguistics - 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